
Useful (can be applied to other domains). Some models of the nervous system are also good at solving real-world problems. Models can be evaluated in terms of how good they are at solving such problems. For example, a model of the visual system might be able to solve challenging problems in computer vision [1]}, [2]}. This assumes that the modeled system in the brain is solving a problem that also appears in technical systems.

Normative (best possible given certain assumptions). Some models provide the optimal solutions to problems that exist in the real world [3]}, [4]}, [5]}, [6]}. Models can be evaluated in terms of how well they represent an optimal solution to a meaningful problem. Normative models are thus often used in domains where behavior or neural properties are expected to be optimal or near optimal [7]}, [8]}, [9]}. For example, a model may ask how well people minimize energy when walking [10]}. Thus, we might ask whether a model supplies the optimal solution to a computational problem faced by the brain and how similar behavior is to these predictions. A normative model can also ask whether the assumed principles underlying the optimality criterion are biologically accurate. This assumes that we can understand the goals of a system and that we gain insight if a system appears to optimize what it is expected to optimize [11]}, [12]}.

Clinically relevant (helps healthcare). Some models produce insights that are relevant for developing or evaluating clinical interventions. Models may be evaluated in terms of how well they generalize to medical problems. For example, simulating individual differences with respect to electrical stimulation enables us to place electrodes to maximize stimulation outcome [13]}. Given the potential to reduce human suffering, there is no doubt that clinical relevance is a meaningful goal. In order for modeling insights to transfer to medicine, a model must be sufficiently similar to the real system.

Inspire experiments (untested assumptions, new hypotheses). Some models change the way we think about a problem and thereby raise interesting new hypotheses via abductive inference [14]}, [15]}. Models can be evaluated in terms of the richness of potential experiments they inspire. For example, a model may suggest that spike timing may affect plasticity and therefore lead to a broad set of tests [16]}, [17]}. A formal model might also uncover hidden assumptions that a field makes when considering a proposed mechanism. To inspire experiments, a set of potential models must be small enough such that experimental tests are meaningful.

Microscopic realism (looks like the brain). Some models describe the microscopic properties of the brain, such as synaptic, pharmacological, and cellular-level properties. Models can then be evaluated in terms of how well they quantitatively describe those properties. For example, models may predict changes in synapses over time [18]}. Commitment to microscopic realism assumes that microscopic properties can be sufficiently decoupled from macroscopic properties such that a reductionist understanding of neural properties is possible [19]}.

Macroscopic realism (looks like the brain at the population level). Some models describe properties of brain areas and networks. Models can then be evaluated in terms of how well they quantitatively describe those properties. For example, models may predict the population activity of brain areas as measured by EEG [20]}. Commitment to macroscopic realism assumes that macroscopic properties can be sufficiently decoupled from finer-scale, distributed properties [21]}.

Behavioral realism (looks like real behavior). Some models can faithfully describe and explain behavioral phenomena. Models can then be evaluated in terms of how well they quantitatively account for behavior. For example, models can predict the way we move our arm as a function of distance we need to travel [22]}. An approach based on behavioral realism supposes that behavior can be understood without a deeper understanding of the brain and that compact models of behavior are possible [23]}, [24]}, [25]}.

Representational (codes like the brain). Some models aim to use representations of information that are similar to representations in the brain. Models can then be evaluated in terms of how well they quantitatively describe representations. For example, models predict that neurons in motor cortex have cosine tuning [26]}. Such modeling assumes that representations can be compactly understood and are the basis of the phenomena we want to understand [27]}.

Compact (few short equations). Some models can be succinctly expressed in mathematical language and/or computer code [28]}, [29]}. Models can then be evaluated in terms of how well they trade off complexity against the quality of description of the phenomena. For example, Fitt's law can compactly describe the balance between speed and precision during hand movements [30]}. This approach assumes that the phenomenon of interest has a low-complexity description [28]}.

Analytically tractable (exact solutions exist). Some models are understandable through mathematical equations as opposed to numerical simulations. Models can then be evaluated in terms of how well they can be analytically solved. For example, models may allow the combination of cues with neurally realistic properties while being analytically solvable [32]}. For scientists with mathematical training, an analytic approach provides a more generalizable understanding compared to numerical models. An implicit assumption is that the system of interest is sufficiently similar to the analytically tractable model such that analyzing one provides insights into the other [33]}.

Interpretable (relates directly to something the brain does). Some models are easily interpreted with respect to how they work (e.g. what outcomes they predict) and/or how the brain might implement the computations. Models can then be evaluated in terms of how well humans can interpret their meaning. For example, units in a simulated system may have receptive fields similar to those of real neurons [34]}, [26]}. For many scientists, prioritizing the interpretability of a model makes the model more relatable to their way of thinking about the brain.

Beauty (elegant). Some models may be symmetrical, balanced, or resonate well with the way we think. Models can then be evaluated in terms of how well they resonate intuitively with their target audience. For example, the same model can be presented in the languages of physics, math, and biology, and can be distinctly useful for these different communities [36]}, [37]}.

